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Deep learning in multimedia healthcare applications: a review

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Abstract

The increase in chronic diseases has affected the countries’ health system and economy. With the recent COVID-19 virus, humanity has experienced a great challenge, which has led to make efforts to detect it and prevent its spread. Hence, it is necessary to develop new solutions that are based on technology and low cost, to satisfy the citizens’ needs. Deep learning techniques is a technological solution that has been used in healthcare lately. Nowadays, with the increase in chips processing capabilities, increase size of data, and the progress in deep learning research, healthcare applications have been proposed to provide citizens’ health needs. In addition, a big amount of data is generated every day. Development in Internet of Things, gadgets, and phones has allowed the access to multimedia data. Data such as images, video, audio and text are used as input of applications based on deep learning methods to support healthcare system to diagnose, predict, or treat patients. This review pretends to give an overview of proposed healthcare solutions based on deep learning techniques using multimedia data. We show the use of deep learning in healthcare, explain the different types of multimedia data, show some relevant deep learning multimedia applications in healthcare, and highlight some challenges in this research area.

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References

  1. W. H. Organization, “World Health Organization,” [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases. [Accessed 4 December 2020].

  2. Yach, D., Hawkes, C., Gould, C.L., Hofman, K.J.: The global burden of chronic diseases: overcoming impediments to prevention and control. J. Amer. Med. Assoc. 291(21), 2616–2622 (2004)

    Article  Google Scholar 

  3. W. H. Organization, “World Health Organization,” [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health. [Accessed 4 December 2020].

  4. KashifNaseer, Q., et al.: Self-assessment and deep learning-based coronavirus detection and medical diagnosis systems for healthcare. Multimed Syst (2021). https://doi.org/10.1007/s00530-021-00839-w

    Article  Google Scholar 

  5. W. H. Organization, "World Health Organization," [Online]. Available: https://www.who.int/emergencies/diseases/novel-coronavirus-2019. [Accessed 8 December 2020].

  6. Alhussein, M., Muhammad, G.: Voice pathology detection using deep learning on mobile healthcare framework. IEEE Access 6, 41034–41041 (2018)

    Article  Google Scholar 

  7. Alhussein, M., Muhammad, G.: Automatic voice pathology monitoring using parallel deep models for smart healthcare. IEEE Access 7, 46474–46479 (2019)

    Article  Google Scholar 

  8. Tobón, D.P., Falk, T.H., Maier, M.: Context awareness in WBANs: a survey on medical and non-medical applications. IEEE Wirel. Commun. 20(4), 30–37 (2013)

    Article  Google Scholar 

  9. Dai, Y., Wang, G., Muhammad, K., Liu, S.: A closed-loop healthcare processing approach based on deep reinforcement learning. Multimed. Tools Appl. 81, 3107–3129 (2022)

    Article  Google Scholar 

  10. Anwer, DN., Ozbay, S.: “Lung Cancer Classification and Detection Using Convolutional Neural Networks.” Proceedings of the 6th International Conference on Engineering & MIS. (2020)

  11. Sieverdes, J.C., Treiber, F., Jenkins, C., Hermayer, K.: Improving diabetes management with mobile health technology. Am. J. Med. Sci. 345(4), 289–295 (2013)

    Article  Google Scholar 

  12. Kirwan, M., Vandelanotte, C., Fenning, A., Duncan, M.J.: Diabetes self-management smartphone application for adults with type 1 diabetes: randomized controlled trial. J. Med. Internet Res. 15(11), e235 (2013)

    Article  Google Scholar 

  13. Maamar, H.R., Boukerche, A., Petriu, E.M.: 3-D streaming supplying partner protocols for mobile collaborative exergaming for health. IEEE Trans. Inf. Technol. Biomed. 16(6), 1079–1095 (2012)

    Article  Google Scholar 

  14. Zhang, Y., Qiu, M., Tsai, C.W., Hassan, M.M., Alamri, A.: Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst. J. 11(1), 88–95 (2017)

    Article  Google Scholar 

  15. Martínez-Pérez, B., de la la TorreDíez, I., López-Coronado, M., Herreros-González, J.: Mobile apps in cardiology: review. JMIR Mhealth Uhealth 1(2), e15 (2013)

    Article  Google Scholar 

  16. Bisio, I., Lavagetto, F., Marchese, M., Sciarrone, A.: A smartphone centric platform for remote health monitoring of heart failure. Int. J. Commun. Syst. 28(11), 1753–1771 (2014)

    Article  Google Scholar 

  17. Fayn, J., Rubel, P.: Toward a personal health society in cardiology. IEEE Trans. Inf. Technol. Biomed. 14(2), 401–409 (2010)

    Article  Google Scholar 

  18. Fontecha, J., Hervás, R., Bravo, J., Navarro, J.F.: A mobile and ubiquitous approach for supporting frailty assessment in elderly people. J. Med. Internet. Res. 15(9), e197 (2013)

    Article  Google Scholar 

  19. Chiarini, G., Ray, P., Akter, S., Masella, C., Ganz, A.: mhealth technologies for chronic diseases and elders: a systematic review. IEEE J. Sel. Areas Commun. 31(9), 6–18 (2013)

    Article  Google Scholar 

  20. Gao, Y., Xiang, X., Xiong, N., Huang, B., Lee, H.J., Alrifai, R., Jiang, X., Fang, Z.: Human action monitoring for healthcare based on deep learning. IEEE Access 6, 52277–52285 (2018)

    Article  Google Scholar 

  21. Zhou, X., Liang, W., Wang, K.I.-K., Wang, H., Yang, L.T., Jin, Q.: Deep-learning-enhanced human activity recognition for internet of healthcare things. IEEE Internet Things J. 7(7), 6429–6438 (2020)

    Article  Google Scholar 

  22. Martinez-Murcia, F.J., Ortiz, A., Gorriz, J.-M., Ramirez, J., Castillo-Barnes, D.: Studying the manifold structure of Alzheimer’s disease: a deep learning approach using convolutional autoencoders. IEEE J. Biomed. Health Inf. 24(1), 17–26 (2020)

    Article  Google Scholar 

  23. Wu, C., Luo, C., Xiong, N., Zhang, W., Kim, T.-H.: A greedy deep learning method for medical disease analysis. IEEE Access 6, 20021–20030 (2018)

    Article  Google Scholar 

  24. Dijcks, JP.: “Oracle: Big data for the enterprise,” 2012. [Online]. Available: http://www.oracle.com/us/products/database/big-data-for-enterprise-519135.pdf. [Accessed 1 December 2020].

  25. Pouyanfar, S., Yang, Y., Chen, S.-C., Shyu, M.-L., Iyengar, S.S.: Multimedia Big data analytics: a survey. ACM Comput. Surv. 51(1), 1–34 (2018)

    Article  Google Scholar 

  26. Halvorsen, P., Riegler, M.A., Schoeffmann, K.: “Medical Multimedia Systems and Applications.” 27th ACM International Conference on Multimedia. (2019)

  27. Hiriyannaiah, S., Akanksh, B.S., Koushik, A.S., Siddesh, G.M., Srinivasa, K.G.: “Deep learning for multimedia data in IoT.” Multimed. Big Data Comput. IoT Appl. pp. 101–129. (2019)

  28. Gumaei, A., Hassan, M.M., Alelaiwi, A., Alsalman, H.: A hybrid deep learning model for human activity recognition using multimodal body sensing data. IEEE Access 7, 99152–99160 (2019)

    Article  Google Scholar 

  29. Chen, S.-C.: Multimedia deep learning. IEEE Multimed 26(1), 5–7 (2019)

    Article  Google Scholar 

  30. Ju, R., Hu, C., Zhou, P., Li, Q.: Early diagnosis of Alzheimer’s disease based on resting-state brain networks and deep learning. IEEE/ACM Trans. Comput. Biol. Bioinf. 16(1), 244–257 (2017)

    Article  Google Scholar 

  31. Muhammed, T., Mehmood, R., Albeshri, A., Katib, I.: UbeHealth: a personalized ubiquitous cloud and edge-enabled networked healthcare system for smart cities. IEEE Access 6, 32258–32285 (2018)

    Article  Google Scholar 

  32. Sierra-Sosa, D., Garcia-Zapirain, B., Castillo, C., Oleagordia, I., Nuño-Solinis, R., Urtaran-Laresgoiti, M., Elmaghraby, A.: Scalable healthcare assessment for diabetic patients using deep learning on multiple GPUs. IEEE Trans. Ind. Inf. 15(10), 5682–5689 (2019)

    Article  Google Scholar 

  33. Aderghal, K., Benois-Pineau, J., Afdel, K., Gwenaëlle, C.: “FuseMe: classification of sMRI images by fusion of Deep CNNs in 2D+ε projections.” 15th International Workshop on Content-Based Multimedia Indexing. (2017).

  34. Shan, F., Gao, Y., Wang, J., Shi, W., Shi N., Han, M., et. al., “Lung infection quantification of COVID-19 in CT images with deep learning.” arXiv:2003.04655. (2020).

  35. Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., et al.: Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology 296(2), 65–67 (2020)

    Article  Google Scholar 

  36. Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z.: Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE/ACM Trans. Comput. Biol. and Bioinf. 18(6), 2775–2780 (2020)

    Article  Google Scholar 

  37. Hu, S., Gao, Y., Niu, Z., Jiang, Y., Li, L., Xiao, X., Wang, M., Fang, E.F., Ye, H.: Weakly supervised deep learning for COVID-19 infection detection and classification from CT images. IEEE Access 8, 118869–118883 (2020)

    Article  Google Scholar 

  38. Shankar K, Eswaran P, Prayag T, et al.: “Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images.” Multimedia Systems. (2021)

  39. Yazhini, K., Loganathan, D.: “A state of art approaches on deep learning models in healthcare: an application perspective.” 3rd International Conference on Trends in Electronics and Informatics (ICOEI), India. (2019)

  40. Yu, Y., Li, M., Liu, L., Li, Y., Wang, J.: Clinical big data and deep learning: applications, challenges, and future outlooks. Big Data Min. Anal. 2(4), 288–305 (2019)

    Article  Google Scholar 

  41. Hung, C.Y., Lin, C.H., Chang, C.S., Li, J.L., Lee, C.C.: “Predicting gastrointestinal bleeding events from multimodal in-hospital electronic health records using deep fusion networks.” 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Germany. (2019)

  42. Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.Z.: Deep learning for health informatics. IEEE Biomed. Health Inf. 21(1), 4–21 (2017)

    Article  Google Scholar 

  43. Ramachandram, D., Taylor, G.W.: Deep multimodal learning: a survey on recent advances and trends. IEEE Signal Process. Mag. 34(6), 96–108 (2017)

    Article  Google Scholar 

  44. Amin, S.U., Hossain, M.S., Muhammad, G., Alhussein, M., Rahman, M.A.: Cognitive smart healthcare for pathology detection and monitoring. IEEE Access 7, 10745–10753 (2019)

    Article  Google Scholar 

  45. LeCun Y., and Bengio, Y.: Convolutional networks for images, speech, and time series, in Handbook of Brain Theory and Neural Networks, USA: M. A. Arbib, ed. Cambridge, MA. (1995)

  46. Li, M., Fei, Z., Zeng, M., Wu, F.-X., Li, Y., Pan, Y., Wang, J.: Automated ICD-9 coding via a deep learning approach. IEEE/ACM Trans. Comput. Biol. Bioinf. 16(4), 1193–1202 (2019)

    Article  Google Scholar 

  47. Yin, W., Yang, X., Zhang, L., Oki, E.: ECG monitoring system integrated with IR-UWB radar based on CNN. IEEE Access 4, 6344–6351 (2016)

    Google Scholar 

  48. Lu, L., Harrison, A.P.: Deep medical image computing in preventive and precision medicine. IEEE Multimedia 25(3), 109–113 (2018)

    Article  Google Scholar 

  49. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: “Densely connected convolutional networks." 2017 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), USA, (2017)

  50. Guo, W., Wang, J., Wang, S.: Deep multimodal representation learning: a survey. IEEE Access 7, 63373–63394 (2019)

    Article  Google Scholar 

  51. Zhang, S.F., Zhai, J.H., Xie, B.J., Zhan Y., Wang, X.: “Multimodal representation learning: advances, trends and challenges.” International Conference on Machine Learning and Cybernetics (ICMLC), Japan. (2019)

  52. Eyben, F., Wöllmer, M., Schuller, B.: “Opensmile: the Munich versatile and fast open-source audio feature extractor.” 18th ACM Int. Conf. Multimedia. (2010).

  53. El-Sawy, A., Bakry, H.E., Loey, M.: “CNN for handwritten Arabic digits recognition based on LeNet-5.” International Conference on Advanced Intelligent Systems and Informatics. (2016)

  54. Minhas, R.A., Javed, A., Irtaza, A., et al.: Shot classification of field sports videos using AlexNet convolutional neural network. Appl. Sci. 9(3), 483 (2019)

    Article  Google Scholar 

  55. Balagourouchetty, L., Pragatheeswaran, J.K., Pottakkat, B., Ramkumar, G.: GoogLeNet-based ensemble FCNet classifier for focal liver lesion diagnosis. IEEE J. Biomed. Health Inf. 24(6), 1686–1694 (2020)

    Article  Google Scholar 

  56. Simonyan K., Zisserman, A.: “Very deep convolutional networks for large-scale image recognition.” Computer Vision and Pattern Recognition. (2016)

  57. Lu, Z., Jiang, X., Kot, A.: Deep coupled resnet for low-resolution face recognition. IEEE Signal Process. Lett. 25(4), 526–530 (2018)

    Article  Google Scholar 

  58. Yang, M., Zhang, L., Feng, X., Zhang, D., “Fisher discrimination dictionary learning for sparse representation.” International Conference on Computer Vision, Spain. (2011)

  59. Baltrušaitis, T., Robinson, P., Morency, L.P., “OpenFace: an open source facial behavior analysis toolkit.” IEEE Winter Conference on Applications of Computer Vision (WACV). (2016)

  60. Burlina, P., Freund, D.E., Joshi, N., Wolfson, Y., Bressler, N.M., “Detection of age-related macular degeneration via deep learning.” IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague. (2016)

  61. Liu, J., Pan, Y., Li, M., Chen, Z., Tang, L., Lu, C., Wang, J.: Applications of deep learning to MRI images: a survey. Big Data Min. Anal. 1(1), 1–18 (2018)

    Article  Google Scholar 

  62. Hu, P., Wu, F., Peng, J., Bao, Y., Chen, F., Kong, D.: Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets. Int. J. Comput. Assist Radiol. Surg. 12(3), 399–411 (2017)

    Article  Google Scholar 

  63. Bar, Y., Diamant, I., Wolf L., Greenspan, H.:“Deep learning with non-medical training used for chest pathology identification.” Medical Imaging: Computer-Aided Diagnosis. (2015)

  64. Che, D., Safran, M., Peng, Z.: “From Big data to big data mining: challenges, issues, and opportunities.” International Conference on Database Systems for Advanced Applications. (2013)

  65. Gandomi, A., Haider, M.: Beyond the hype: Big data concepts, methods, and analytics. Int. J. Inf. Manag. 35(2), 133–144 (2015)

    Article  Google Scholar 

  66. Ye, Z., Tafti, A.P., He, K.Y., Wang, K., He, M.M.: Sparktext: biomedical text mining on big data framework. PLoS ONE 11(9), e0162721 (2016)

    Article  Google Scholar 

  67. Leibetseder, A., Petscharnig, S., Primus M.J., et. Al.: “Lapgyn4: a dataset for 4 automatic content analysis problems in the domain of laparoscopic gynecology.” 9th ACM Multimedia Systems Conference. (2018)

  68. Pogorelov, K., Randel, K.R., de Lange, T., et. al, “Nerthus: a bowel preparation quality video dataset.” 8th ACM on Multimedia Systems Conference. (2017)

  69. Pogorelov, K., Randel, K.R., Griwodz C., et Al.: “Kvasir: a multi-class image data set for computer aided gastrointestinal disease detection.” ACM Multimedia Systems(MMSYS). (2017)

  70. Schoeffmann, K., Taschwer, M., Sarny, S., et al., “Cataract-101--video dataset of101 cataract surgeries.” ACM International Conference on Multimedia Retrieval (ICMR). (2018)

  71. Nguyen, P., Tran, T., Wickramasinghe, N., Venkatesh, S.: Deepr: a convolutional net for medical records. IEEE J. Biomed. Health Inf. 21(1), 22–30 (2017)

    Article  Google Scholar 

  72. Choi, E., Bahadori, M.T., Schuetz, A., Stewart, W.F., Sun, J., “Doctor ai: Predicting clinical events via recurrent neural networks.” 1st Mach. Learn. Healthcare Conf. (2016)

  73. Guo, H., Zhang, Y.: Resting state fMRI and improved deep learning algorithm for earlier detection of Alzheimer’s disease. IEEE Access 8, 115383–115392 (2020)

    Article  Google Scholar 

  74. Huang, C., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 395(10223), 497–506 (2020)

    Article  Google Scholar 

  75. Wang, D., Hu, B., Hu, C., et al.: Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 323(11), 1061 (2020)

    Article  Google Scholar 

  76. Varela-Santos, S., Melin, P.: A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks. Inf. Sci. 545, 403–414 (2020)

    Article  MathSciNet  Google Scholar 

  77. Bankman, I.: Handbook of medical image processing and analysis, San Diego, CA, USA: second ed., Academic Press. (2008)

  78. Ismael, A.M., Sengür, A.: Deep learning approaches for COVID-19 detection based on chest X-ray images. Exp Syst. Appl. 164, 114054 (2020)

    Article  Google Scholar 

  79. Wang, S.-H., Nayak, D.R., Guttery, D.S., et al.: COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis. Inf. Fusio 68, 131–148 (2020)

    Article  Google Scholar 

  80. Shorfuzzaman, M., and Hossain, M.S.: MetaCOVID: a siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients. Pattern Recognit. 113, 107700 (2020)

    Article  Google Scholar 

  81. Hossain, M.S., Muhammad, G., Guizani, N.: Explainable AI and mass surveillance system-based healthcare framework to combat COVID-i9 like pandemics. IEEE Netw. 34(4), 126–132 (2020)

    Article  Google Scholar 

  82. Yunus, R., Arif, O., Afzal, H., Amjad, M.F., Abbas, H., Bokhari, H.N., et al.: A framework to estimate the nutritional value of food in real time using deep learning techniques. IEEE Access 7, 2643–2652 (2018)

    Article  Google Scholar 

  83. Mikolov, T., Chen, K., Corrado G., Dean, J., “Efficient estimation of word representations in vector space.” Computation and Language. (2013)

  84. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., “Rethinking the inception architecture for computer vision.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (2016)

  85. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: “Inception-v4, Inception-ResNet and the impact of residual connections on learning.” Computer Vision and Pattern Recognition. (2016)

  86. Cheng, G., Wan, Y., Saudagar, A.N., Namuduri, K., Buckles, B.P.: “Advances in human action recognition: a survey.” Computer Vision and Pattern Recognition. (2015)

  87. Bernal, E.A., Yang, X., Li, Q., Kumar, J., Madhvanath, S., Ramesh, P., Bala, R.: Deep temporal multimodal fusion for medical procedure monitoring using wearable sensors. IEEE Trans. Multimed. 20(1), 107–118 (2018)

    Article  Google Scholar 

  88. Kumar, J., Li, Q., Kyal, S., Bernal, E.A., Bala, R.: “On-the-Fly Hand detection training with application in egocentric action recognition.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. (2015)

  89. Shojaei-Hashemi, A., Nasiopoulos, P., Little, J.J., Pourazad, M.T., “Video-based human fall detection in smart homes using deep learning.” IEEE International Symposium on Circuits and Systems (ISCAS), Italy. (2018)

  90. Shahroudy, A., Liu, J., Ng, T.T., Wang, G.:“NTU RGB+D: a large scale dataset for 3d human activity analysis.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (2016)

  91. Muhammad, K., Khan, S., Ser, J.D., de Albuquerque, VHC.: “Deep learning for multigrade brain tumor classification in smart healthcare systems: a prospective survey.” IEEE Transactions on Neural Networks and Learning Systems. Early Access. pp. 1–16 (2020).

  92. Abadi, M.: “TensorFlow: learning functions at scale.” 21st ACM SIGPLAN International Conference on Functional. (2016)

  93. Rasiwasia, N., Pereira, J.C., Coviello E., et. al: “A new approach to cross-modal multimedia retrieval.” 18th ACM international conference on Multimedia. (2010)

  94. Zhang, J., Han, Y., Tang, J., Hu, Q., Jiang, J.: Semi-supervised image-to-video adaptation for video action recognition. IEEE Trans. Cybern. 47(4), 960–973 (2017)

    Article  Google Scholar 

  95. Pan, S.J., Yang, Q.: A Survey on Transfer Learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)

    Article  Google Scholar 

  96. He K., Zhang, X., Ren, S., Sun, J.: “Deep residual learning for image recognition.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (2016)

  97. Pennington, J., Socher, R., Manning, C. D.: “GloVe: Global vectors for word representation.” Conf. Empirical Methods Natural Lang. Process. (2014).

  98. Riegler, M., Lux, M., Griwodz C., et. Al: “Multimedia and medicine: teammates for better disease detection and survival.” 24th ACM international conference on Multimedia. (2016)

  99. Saddik, A.E.: Digital twins: the convergence of multimedia technologies. IEEE Multimedia 25(2), 87–92 (2018)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Telecommunications Engineering, Universidad de Medellín, Medellín, Colombia

    Diana P. Tobón V

  2. Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    M. Shamim Hossain

  3. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Ghulam Muhammad

  4. Head of Research Department - ICT (IoT Digital Platforms, Data Analytics & Artificial Intelligence) IKERLAN, Arrasate, Spain

    Josu Bilbao

  5. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

    Abdulmotaleb El Saddik

  6. University of Ottawa, Ottawa, Canada

    Abdulmotaleb El Saddik

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  1. Diana P. Tobón V
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  2. M. Shamim Hossain
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Tobón, D.P., Hossain, M.S., Muhammad, G. et al. Deep learning in multimedia healthcare applications: a review. Multimedia Systems 28, 1465–1479 (2022). https://doi.org/10.1007/s00530-022-00948-0

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